2022_06_17_Find Induced Resistance

#Set working directory to appropriate folder for inputs and outputs on Google Drive

Initialize

Assign directory and load files

Look at markers of induced resistant lineages (versus all? versus sensitive? difference is lineages that were resistant to both independently)

expression of end resistant state markers in the induced resistant lineages

do cells from these lineages cluster together?

are cells from these lineages resistant to many or few conditions?

how else do these lineages distinguish themselves?

do we want to do a pathway analysis here?

Build new object that defines induced resistance from gDNA_collapsed

cross check against gDNA_cDNA_collapsed to look at things with cDNA representation

load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/all_data_final_lineages.RData') #seurat object with final lineage assignments

load('2022_01_14_analysis_scripts/2022_05_27_analysis/Preprocess_gDNA/preprocessed_gDNA.RData') # need list of all gDNA reads per lineage

load('2022_01_14_analysis_scripts/2022_05_27_analysis/Filtering_cDNA/filtered_cDNA.RData') # need list of all lineages with cDNA representation
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Filtering_cDNA/resistant_lineage_lists.RData') # list of lineages passing filtering
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/all_data_final_lineages.RData') # Load in the single cell object

#How many reads for each of these lineages? How many cells? Plot distribution of lineage size (cells? reads? which time?), correlation between time 1 and 2

nofilter_gDNA_lins <- list()
for (i in names(gDNA_collapsed)){
  if (i != 'FirstSample'){
  nofilter_gDNA_lins[[i]] <- gDNA_collapsed[[i]]$Lineage[gDNA_collapsed[[i]]$Reads > 1]
  }
}


dabtram_cocl2 <- list(dabtram = unique(c(nofilter_gDNA_lins$DabTram, nofilter_gDNA_lins$DabTramtoCis, nofilter_gDNA_lins$DabTramtoCoCl2, nofilter_gDNA_lins$DabTramtoDabTram)), 
                           cocl2 = unique(c(nofilter_gDNA_lins$CoCl2, nofilter_gDNA_lins$CoCl2toCis, nofilter_gDNA_lins$CoCl2toCoCl2, nofilter_gDNA_lins$CoCl2toDabTram)),
                           dabtramtococl2 = nofilter_gDNA_lins$DabTramtoCoCl2,
                           cocl2todabtram = nofilter_gDNA_lins$CoCl2toDabTram)

dabtram_cis <- list(dabtram = unique(c(nofilter_gDNA_lins$DabTram, nofilter_gDNA_lins$DabTramtoCis, nofilter_gDNA_lins$DabTramtoCoCl2, nofilter_gDNA_lins$DabTramtoDabTram)), 
                         cis = unique(c(nofilter_gDNA_lins$Cis, nofilter_gDNA_lins$CistoCis, nofilter_gDNA_lins$CistoCoCl2, nofilter_gDNA_lins$CistoDabTram)),
                         dabtramtocis = nofilter_gDNA_lins$DabTramtoCis,
                         cistodabtram = nofilter_gDNA_lins$CistoDabTram)

cocl2_cis <- list(cocl2 = unique(c(nofilter_gDNA_lins$CoCl2, nofilter_gDNA_lins$CoCl2toCis, nofilter_gDNA_lins$CoCl2toCoCl2, nofilter_gDNA_lins$CoCl2toDabTram)),
                       cis = (c(nofilter_gDNA_lins$Cis, nofilter_gDNA_lins$CistoCis, nofilter_gDNA_lins$CistoCoCl2, nofilter_gDNA_lins$CistoDabTram)),
                       cocl2tocis = nofilter_gDNA_lins$CoCl2toCis,
                       cistococl2 = nofilter_gDNA_lins$CistoCoCl2)

induced_resistant_lins <- list()

dabtram_cocl2_venn <- venn(dabtram_cocl2)

dabtram_cis_venn <- venn(dabtram_cis)

cocl2_cis_venn <- venn(cocl2_cis)

induced_resistant_lins[['DabTram_Inducedto_CoCl2']] = attr(dabtram_cocl2_venn, "intersections")$'dabtram:dabtramtococl2'
induced_resistant_lins[['CoCl2_Inducedto_DabTram']] = attr(dabtram_cocl2_venn, "intersections")$'cocl2:cocl2todabtram'

induced_resistant_lins[['DabTram_Inducedto_Cis']] = attr(dabtram_cis_venn, "intersections")$'dabtram:dabtramtocis'
induced_resistant_lins[['Cis_Inducedto_DabTram']] = attr(dabtram_cis_venn, "intersections")$'cis:cistodabtram'

induced_resistant_lins[['CoCl2_Inducedto_Cis']] = attr(cocl2_cis_venn, "intersections")$'cocl2:cocl2tocis'
induced_resistant_lins[['Cis_Inducedto_CoCl2']] = attr(cocl2_cis_venn, "intersections")$'cis:cistococl2'

#plotting cell # correlation between time 1 and 2

induced_resistant_df <- list()

for (i in names(induced_resistant_lins)){
  split <- strsplit(i, '_')
  time1 <- split[[1]][1]
  time2 <- paste0(split[[1]][1], 'to', split[[1]][3])
  
  test <- filter(gDNA_collapsed[[time1]], gDNA_collapsed[[time1]]$Lineage %in% induced_resistant_lins[[i]])
  colnames(test)[colnames(test) == "Reads"] <- "Reads_time1"
  colnames(test)[colnames(test) == "RPM"] <- "RPM_time1"

  test$Num_Cells_time1 <- rep(0, length(test$Lineage))
  test$Num_Cells_time2 <- rep(0, length(test$Lineage))
  test$Reads_time2 <- rep(0, length(test$Lineage))
  
  for (k in 1:length(test$Lineage)){
    lin <- test$Lineage[[k]]
  
    if (is.element(lin, gDNA_cDNA_collapsed[[time1]]$Lineage)){
      test$Num_Cells_time1[[k]] <- gDNA_cDNA_collapsed[[time1]]$Num_Cells[gDNA_cDNA_collapsed[[time1]]$Lineage == lin]
    }
    if (is.element(lin, gDNA_cDNA_collapsed[[time2]]$Lineage)){
      test$Num_Cells_time2[[k]] <- gDNA_cDNA_collapsed[[time2]]$Num_Cells[gDNA_cDNA_collapsed[[time2]]$Lineage == lin]
    }
    test$Reads_time2[[k]] <- gDNA_collapsed[[time2]]$Reads[gDNA_collapsed[[time2]]$Lineage == lin]
    
    test$Num_Cells_time1_norm[[k]] <- test$Num_Cells_time1[[k]] / length(all_data$OG_condition == time1)
    
  }
  induced_resistant_df[[i]] <- test  
}

Find gene expression markers of dabtram induced to cocl2

for (i in names(induced_resistant_df)){
  print(ggplot(induced_resistant_df[[i]], aes(x = Num_Cells_time2, y = Num_Cells_time1)) + geom_point() + ggtitle(paste("per lineage # cells at time 1 vs time 2 in", i)))
}

# for (i in names(cDNA_excluded)){
#   print(ggplot(data = cDNA_excluded[[i]], aes(x = Num_Cells)) + geom_bar() + labs(x = 'Number of Cells per lineage', title = paste(i, "cDNA lineages lost in filtering")))
#   print(ggplot(data = gDNA_cDNA_collapsed[[i]], aes(x = Num_Cells)) + geom_bar() + labs(x = 'Number of Cells per lineage ', title = paste(i, "cDNA lineages prior to filtering")))      
# }
# dev.off()

Find gene expression markers of cocl2 induced to dabtram

# Finding the lineages that end up induced resistant to cocl2, in the dabtram object
Dabtram_inducedto_CoCl2 <- names(all_data$Lineage[all_data$OG_condition == 'dabtram' & all_data$Lineage %in% induced_resistant_lins$DabTram_Inducedto_CoCl2])
# Finding the lineages did not 
Dabtram_not_inducedto_CoCl2 <- names(all_data$Lineage[all_data$OG_condition == 'dabtram' & all_data$Lineage %in% attr(dabtram_cocl2_venn, "intersections")$'dabtram'])

# Find induced markers
dabtram_inducedto_cocl2_markers <- FindMarkers(all_data, ident.1 = Dabtram_inducedto_CoCl2, ident.2 = Dabtram_not_inducedto_CoCl2, logfc.threshold = 0.25)

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# Also want to plot these lineages on umap along with induced resistance genes
Idents(all_data) <- all_data$OG_condition
dabtram <- subset(all_data, idents = 'dabtram')
dabtram <- FindNeighbors(dabtram, dims = 1:10)

Computing nearest neighbor graph
Computing SNN

dabtram <- FindClusters(dabtram, resolution = 0.5)

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 3203
Number of edges: 108151

Running Louvain algorithm...

0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

Maximum modularity in 10 random starts: 0.8611
Number of communities: 7
Elapsed time: 0 seconds

dabtram <- RunUMAP(dabtram, dims = 1:10)

15:27:24 UMAP embedding parameters a = 0.9922 b = 1.112
15:27:24 Read 3203 rows and found 10 numeric columns
15:27:24 Using Annoy for neighbor search, n_neighbors = 30
15:27:24 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:27:24 Writing NN index file to temp file /var/folders/ph/24prrxys02179y9_qzhxjgvc0000gn/T//Rtmp85KPIJ/file1376631b15490
15:27:24 Searching Annoy index using 1 thread, search_k = 3000
15:27:25 Annoy recall = 100%
15:27:26 Commencing smooth kNN distance calibration using 1 thread
15:27:28 Initializing from normalized Laplacian + noise
15:27:28 Commencing optimization for 500 epochs, with 133908 positive edges
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:27:33 Optimization finished

DimPlot(dabtram, reduction = 'umap')

FeaturePlot(dabtram, features = c("IGFBP5","COL1A1","CAV1"))

DimPlot(dabtram, cells.highlight = Dabtram_inducedto_CoCl2, cols.highlight = c('red'))

DimPlot(dabtram, cells.highlight = Dabtram_not_inducedto_CoCl2, cols.highlight = c('blue'))

Find gene expression markers of dabtram induced to cis

# Finding the lineages that end up induced resistant to cocl2, in the dabtram object
CoCl2_inducedto_Dabtram <- names(all_data$Lineage[all_data$OG_condition == 'cocl2' & all_data$Lineage %in% induced_resistant_lins$CoCl2_Inducedto_DabTram])
# Finding the lineages did not 
CoCl2_not_inducedto_Dabtram <- names(all_data$Lineage[all_data$OG_condition == 'cocl2' & all_data$Lineage %in% attr(dabtram_cocl2_venn, "intersections")$'cocl2'])

# Find induced markers
cocl2_inducedto_dabtram_markers <- FindMarkers(all_data, ident.1 = CoCl2_inducedto_Dabtram, ident.2 = CoCl2_not_inducedto_Dabtram, logfc.threshold = 0.25)

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# Also want to plot these lineages on umap along with induced resistance genes
Idents(all_data) <- all_data$OG_condition
cocl2 <- subset(all_data, idents = 'cocl2')
cocl2 <- FindNeighbors(cocl2, dims = 1:10)

Computing nearest neighbor graph
Computing SNN

cocl2 <- FindClusters(cocl2, resolution = 0.5)

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 4823
Number of edges: 160093

Running Louvain algorithm...

0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

Maximum modularity in 10 random starts: 0.8374
Number of communities: 9
Elapsed time: 0 seconds

cocl2 <- RunUMAP(cocl2, dims = 1:10)

15:27:38 UMAP embedding parameters a = 0.9922 b = 1.112
15:27:38 Read 4823 rows and found 10 numeric columns
15:27:38 Using Annoy for neighbor search, n_neighbors = 30
15:27:38 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:27:39 Writing NN index file to temp file /var/folders/ph/24prrxys02179y9_qzhxjgvc0000gn/T//Rtmp85KPIJ/file137667126d36c
15:27:39 Searching Annoy index using 1 thread, search_k = 3000
15:27:40 Annoy recall = 100%
15:27:41 Commencing smooth kNN distance calibration using 1 thread
15:27:43 Initializing from normalized Laplacian + noise
15:27:43 Commencing optimization for 500 epochs, with 195924 positive edges
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:27:50 Optimization finished

DimPlot(cocl2, reduction = 'umap')

FeaturePlot(cocl2, features = c("RAMP1","CYTL1","MT-CO3"))

DimPlot(cocl2, cells.highlight = CoCl2_inducedto_Dabtram, cols.highlight = c('red'))

DimPlot(cocl2, cells.highlight = CoCl2_not_inducedto_Dabtram, cols.highlight = c('blue'))

Find gene expression markers of cocl2 induced to cis

# Finding the lineages that end up induced resistant to cocl2, in the dabtram object
Dabtram_inducedto_Cis <- names(all_data$Lineage[all_data$OG_condition == 'dabtram' & all_data$Lineage %in% induced_resistant_lins$DabTram_Inducedto_Cis])
# Finding the lineages did not 
Dabtram_not_inducedto_Cis <- names(all_data$Lineage[all_data$OG_condition == 'dabtram' & all_data$Lineage %in% attr(dabtram_cis_venn, "intersections")$'dabtram'])

# Find induced markers
dabtram_inducedto_cis_markers <- FindMarkers(all_data, ident.1 = Dabtram_inducedto_Cis, ident.2 = Dabtram_not_inducedto_Cis, logfc.threshold = 0.25)

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# Also want to plot these lineages on umap along with induced resistance genes
Idents(all_data) <- all_data$OG_condition
dabtram <- subset(all_data, idents = 'dabtram')
dabtram <- FindNeighbors(dabtram, dims = 1:10)

Computing nearest neighbor graph
Computing SNN

dabtram <- FindClusters(dabtram, resolution = 0.5)

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 3203
Number of edges: 108151

Running Louvain algorithm...

0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

Maximum modularity in 10 random starts: 0.8611
Number of communities: 7
Elapsed time: 0 seconds

dabtram <- RunUMAP(dabtram, dims = 1:10)

15:27:55 UMAP embedding parameters a = 0.9922 b = 1.112
15:27:55 Read 3203 rows and found 10 numeric columns
15:27:55 Using Annoy for neighbor search, n_neighbors = 30
15:27:55 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:27:55 Writing NN index file to temp file /var/folders/ph/24prrxys02179y9_qzhxjgvc0000gn/T//Rtmp85KPIJ/file137662c02917d
15:27:55 Searching Annoy index using 1 thread, search_k = 3000
15:27:56 Annoy recall = 100%
15:27:57 Commencing smooth kNN distance calibration using 1 thread
15:27:59 Initializing from normalized Laplacian + noise
15:27:59 Commencing optimization for 500 epochs, with 133908 positive edges
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:28:04 Optimization finished

DimPlot(dabtram, reduction = 'umap')

FeaturePlot(dabtram, features = c("PMEL","A2M","CTSK"))

DimPlot(dabtram, cells.highlight = Dabtram_inducedto_Cis, cols.highlight = c('red'))

DimPlot(dabtram, cells.highlight = Dabtram_not_inducedto_Cis, cols.highlight = c('blue'))

Find gene expression markers of cis induced to dabtram

# Finding the lineages that end up induced resistant to cocl2, in the cis object
CoCl2_inducedto_Cis <- names(all_data$Lineage[all_data$OG_condition == 'cocl2' & all_data$Lineage %in% induced_resistant_lins$CoCl2_Inducedto_Cis])
# Finding the lineages did not 
CoCl2_not_inducedto_Cis <- names(all_data$Lineage[all_data$OG_condition == 'cocl2' & all_data$Lineage %in% attr(cocl2_cis_venn, "intersections")$'cocl2'])

# Find induced markers
cocl2_inducedto_cis_markers <- FindMarkers(all_data, ident.1 = CoCl2_inducedto_Cis, ident.2 = CoCl2_not_inducedto_Cis, logfc.threshold = 0.25)

  |                                                  | 0 % ~calculating  
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# Also want to plot these lineages on umap along with induced resistance genes
Idents(all_data) <- all_data$OG_condition
cocl2 <- subset(all_data, idents = 'cocl2')
cocl2 <- FindNeighbors(cocl2, dims = 1:10)

Computing nearest neighbor graph
Computing SNN

cocl2 <- FindClusters(cocl2, resolution = 0.5)

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 4823
Number of edges: 160093

Running Louvain algorithm...

0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

Maximum modularity in 10 random starts: 0.8374
Number of communities: 9
Elapsed time: 0 seconds

cocl2 <- RunUMAP(cocl2, dims = 1:10)

15:28:10 UMAP embedding parameters a = 0.9922 b = 1.112
15:28:10 Read 4823 rows and found 10 numeric columns
15:28:10 Using Annoy for neighbor search, n_neighbors = 30
15:28:10 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:28:10 Writing NN index file to temp file /var/folders/ph/24prrxys02179y9_qzhxjgvc0000gn/T//Rtmp85KPIJ/file137665c9dbc1d
15:28:10 Searching Annoy index using 1 thread, search_k = 3000
15:28:11 Annoy recall = 100%
15:28:12 Commencing smooth kNN distance calibration using 1 thread
15:28:14 Initializing from normalized Laplacian + noise
15:28:14 Commencing optimization for 500 epochs, with 195924 positive edges
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:28:21 Optimization finished

DimPlot(cocl2, reduction = 'umap')

FeaturePlot(cocl2, features = c("PMEL","A2M","CTSK"))

DimPlot(cocl2, cells.highlight = CoCl2_inducedto_Cis, cols.highlight = c('red'))

DimPlot(cocl2, cells.highlight = CoCl2_not_inducedto_Cis, cols.highlight = c('blue'))

Find gene expression markers of cis induced to cocl2

# Finding the lineages that end up induced resistant to cocl2, in the dabtram object
Cis_inducedto_Dabtram <- names(all_data$Lineage[all_data$OG_condition == 'cis' & all_data$Lineage %in% induced_resistant_lins$Cis_Inducedto_DabTram])
# Finding the lineages did not 
Cis_not_inducedto_Dabtram <- names(all_data$Lineage[all_data$OG_condition == 'cis' & all_data$Lineage %in% attr(dabtram_cis_venn, "intersections")$'cis'])

# Find induced markers
cis_inducedto_dabtram_markers <- FindMarkers(all_data, ident.1 = Cis_inducedto_Dabtram, ident.2 = Cis_not_inducedto_Dabtram, logfc.threshold = 0.25)

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  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s  

# Also want to plot these lineages on umap along with induced resistance genes
Idents(all_data) <- all_data$OG_condition
cis <- subset(all_data, idents = 'cis')
cis <- FindNeighbors(cis, dims = 1:10)

Computing nearest neighbor graph
Computing SNN

cis <- FindClusters(cis, resolution = 0.5)

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 8303
Number of edges: 244918

Running Louvain algorithm...

0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

Maximum modularity in 10 random starts: 0.8046
Number of communities: 8
Elapsed time: 1 seconds

cis <- RunUMAP(cis, dims = 1:10)

15:28:29 UMAP embedding parameters a = 0.9922 b = 1.112
15:28:29 Read 8303 rows and found 10 numeric columns
15:28:29 Using Annoy for neighbor search, n_neighbors = 30
15:28:29 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:28:30 Writing NN index file to temp file /var/folders/ph/24prrxys02179y9_qzhxjgvc0000gn/T//Rtmp85KPIJ/file1376677a9436b
15:28:30 Searching Annoy index using 1 thread, search_k = 3000
15:28:33 Annoy recall = 100%
15:28:33 Commencing smooth kNN distance calibration using 1 thread
15:28:35 Initializing from normalized Laplacian + noise
15:28:35 Commencing optimization for 500 epochs, with 350988 positive edges
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:28:47 Optimization finished

DimPlot(cis, reduction = 'umap')

FeaturePlot(cis, features = c("HIST1H1D","CYP51A1","LDHA","HIST1H1B"))

DimPlot(cis, cells.highlight = Cis_inducedto_Dabtram, cols.highlight = c('red'))

DimPlot(cis, cells.highlight = Cis_not_inducedto_Dabtram, cols.highlight = c('blue'))